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Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies
Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-sca...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044136/ https://www.ncbi.nlm.nih.gov/pubmed/33850243 http://dx.doi.org/10.1038/s41746-021-00428-1 |
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author | Shang, Ning Khan, Atlas Polubriaginof, Fernanda Zanoni, Francesca Mehl, Karla Fasel, David Drawz, Paul E. Carrol, Robert J. Denny, Joshua C. Hathcock, Matthew A. Arruda-Olson, Adelaide M. Peissig, Peggy L. Dart, Richard A. Brilliant, Murray H. Larson, Eric B. Carrell, David S. Pendergrass, Sarah Verma, Shefali Setia Ritchie, Marylyn D. Benoit, Barbara Gainer, Vivian S. Karlson, Elizabeth W. Gordon, Adam S. Jarvik, Gail P. Stanaway, Ian B. Crosslin, David R. Mohan, Sumit Ionita-Laza, Iuliana Tatonetti, Nicholas P. Gharavi, Ali G. Hripcsak, George Weng, Chunhua Kiryluk, Krzysztof |
author_facet | Shang, Ning Khan, Atlas Polubriaginof, Fernanda Zanoni, Francesca Mehl, Karla Fasel, David Drawz, Paul E. Carrol, Robert J. Denny, Joshua C. Hathcock, Matthew A. Arruda-Olson, Adelaide M. Peissig, Peggy L. Dart, Richard A. Brilliant, Murray H. Larson, Eric B. Carrell, David S. Pendergrass, Sarah Verma, Shefali Setia Ritchie, Marylyn D. Benoit, Barbara Gainer, Vivian S. Karlson, Elizabeth W. Gordon, Adam S. Jarvik, Gail P. Stanaway, Ian B. Crosslin, David R. Mohan, Sumit Ionita-Laza, Iuliana Tatonetti, Nicholas P. Gharavi, Ali G. Hripcsak, George Weng, Chunhua Kiryluk, Krzysztof |
author_sort | Shang, Ning |
collection | PubMed |
description | Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate (“A-by-G” grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies. |
format | Online Article Text |
id | pubmed-8044136 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80441362021-04-28 Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies Shang, Ning Khan, Atlas Polubriaginof, Fernanda Zanoni, Francesca Mehl, Karla Fasel, David Drawz, Paul E. Carrol, Robert J. Denny, Joshua C. Hathcock, Matthew A. Arruda-Olson, Adelaide M. Peissig, Peggy L. Dart, Richard A. Brilliant, Murray H. Larson, Eric B. Carrell, David S. Pendergrass, Sarah Verma, Shefali Setia Ritchie, Marylyn D. Benoit, Barbara Gainer, Vivian S. Karlson, Elizabeth W. Gordon, Adam S. Jarvik, Gail P. Stanaway, Ian B. Crosslin, David R. Mohan, Sumit Ionita-Laza, Iuliana Tatonetti, Nicholas P. Gharavi, Ali G. Hripcsak, George Weng, Chunhua Kiryluk, Krzysztof NPJ Digit Med Article Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate (“A-by-G” grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies. Nature Publishing Group UK 2021-04-13 /pmc/articles/PMC8044136/ /pubmed/33850243 http://dx.doi.org/10.1038/s41746-021-00428-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Shang, Ning Khan, Atlas Polubriaginof, Fernanda Zanoni, Francesca Mehl, Karla Fasel, David Drawz, Paul E. Carrol, Robert J. Denny, Joshua C. Hathcock, Matthew A. Arruda-Olson, Adelaide M. Peissig, Peggy L. Dart, Richard A. Brilliant, Murray H. Larson, Eric B. Carrell, David S. Pendergrass, Sarah Verma, Shefali Setia Ritchie, Marylyn D. Benoit, Barbara Gainer, Vivian S. Karlson, Elizabeth W. Gordon, Adam S. Jarvik, Gail P. Stanaway, Ian B. Crosslin, David R. Mohan, Sumit Ionita-Laza, Iuliana Tatonetti, Nicholas P. Gharavi, Ali G. Hripcsak, George Weng, Chunhua Kiryluk, Krzysztof Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies |
title | Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies |
title_full | Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies |
title_fullStr | Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies |
title_full_unstemmed | Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies |
title_short | Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies |
title_sort | medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044136/ https://www.ncbi.nlm.nih.gov/pubmed/33850243 http://dx.doi.org/10.1038/s41746-021-00428-1 |
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